The Design of Adaptive Immune Genetic Algorithm Based on Vector Distance
Aiming at the problems which exist in Genetic Algorithm (GA), including reduction in diversity, premature ness, weak local searching ability and slow convergence rate, this paper studies the effect of antigens recognition module, immune memory module antibodies self-adjusting module of Immune Algorithm, and adaptive probability crossover and mutation operator to GA and proposes Adaptive Immune Genetic Algorithm (AIGA) based on vector distance. After exploration, this paper solves the problems in GA above. This paper takes a controlled object as example to test the effect of each module to GA by simulation. Simulation results show that the four modules effectively improve the drawbacks of GA. At the same time, this paper proves the convergence of the algorithm, and verifies algorithm by testing function. Simulation results show that AIGA is better than GA on global optimization capability. The algorithm can obtain the optimal solution with high fitness value, and also has good convergence stability.
Genetic Algorithm Adaptive Immune Genetic Algorithm Testimony of convergence
Guili Yuan Yan-guang Xue Jizhen Liu Qingjiao Liang
school of control and computer engineering, North China Electric Power University
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
2675-2680
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）